使用视网膜成像和尿数据诊断慢性病:多模式深度学习方法
Youngmin Bhak1,2, Yu Ho Lee3, Joonhyung Kim4
1Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea.
JMIR medical informatics
|February 9, 2025
概括
将视网膜图像和尿液数据集成到深度学习模型中,可以提高慢性病 (CKD) 的检测. 多模式深度学习 (MDL) 显示出对非侵入性CKD查的前景,其表现优于仅使用视网膜图像的模型.
科学领域:
- 眼科医生 眼科 眼科
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
背景情况:
- 慢性病 (CKD) 是一种普遍的疾病,需要及早检测以预防并发症.
- 使用视网膜图像的深度学习 (DL) 模型为CKD提供了潜在的非侵入性查方法.
- 目前的DL模型可能在检测蛋白尿和在特定子组中的表现方面存在局限性.
研究的目的:
- 评估在DL模型中结合视网膜图像和尿液测量棒数据的有效性,以改善CKD诊断.
- 为了比较多式联机DL模型与单式联机模型的性能.
主要方法:
- 开发并验证了三个模型:eGFR-RIDL (视网膜图像),eGFR-UDLR (尿液数据) 和eGFR-MMDL (结合视网膜图像和尿液数据).
- 所有模型都预测了使用2009年CKD-EPI方程式的估计球透率 (eGFR) <60mL/min/1.73m2.
- 利用多中心数据集与开发 (65,082) 和外部验证 (58,284) 队列;采用宽剩余网络和突出地图.
主要成果:
- 在测试和验证组中,eGFR-MMDL模型表现出高于eGFR-RIDL的性能 (AUC为0.94比0.90和0.88比0.77).
- 虽然eGFR-UDLR表现与eGFR-MMDL相提并论,尤其是在外部验证中,但eGFR-MMDL在所有子组中都有所改善.
- eGFR-MMDL在65岁以下或蛋白尿症患者中表现出色,这表明综合数据的协同效益.
结论:
- 综合视网膜图像和尿液数据的多式DL (MDL) 模型显示了对非侵入性CKD查的重大前景.
- 只有视网膜图像模型的MDL表现优于视网膜图像模型,突出显示了组合数据模式的价值.
- 对于65岁及以上的个人,仍建议进行常规血液检查,因为该模型在这个人口群体中的性能限制.
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